ABSTRACT
Objectives
Atrial fibrillation (AF) is a common clinical arrhythmia. Failure to identify AF leaves patients at a considerably higher risk of disability and death, making its early screening and prevention extremely important. This study aimed to investigate knowledge, attitude, and practice (KAP) towards AF prevention and screening among the high‐risk population.
Methods
Individuals at a high risk of AF were required to fill out questionnaires between August 2022 and October 2022.
Results
A total of 863 participants aged 66.54 ± 11.00 years were enrolled. The median scores of knowledge, attitude, and practice were 9 (81.8% of maximum), 37 (64.9% of maximum), and 21.56 (86.2% of maximum), respectively. Only knowledge (OR = 1.159, 95% confidence interval (CI): 1.045–1.286, p = 0.005) was independently associated with better practice, while attitude (OR = 0.861, 95% CI: 0.817–0.908, p < 0.001), reported poor sleep quality (OR = 0.529, 95% CI: 0.259–1.0788, p = 0.010), fair (OR = 0.234, 95% CI: 0.111–0.491, p < 0.001) or fluctuating (OR = 0.321, 95% CI: 0.103–0.996, p = 0.049) emotional state, and acquiring the questionnaire through social media (OR = 0.419, 95% CI: 0.268–0.653, p < 0.001) were independently associated with worse practice.
Conclusions
Participats from high‐risk AF population in this study demonstrated acceptable practice towards AF screening, mostly moderated by knowledge and attitude. Some gaps in knowledge could be improved by targeted health education, especially important in vulnerable subpopulations.
Keywords: atrial fibrillation, atrial fibrillation screening, attitude, high‐risk elderly population, knowledge, practice
Distribution of KAP scores for participants ≤ 65 versus > 65 years.
Abbreviations
- AF
Atrial fibrillation
- APHRS
Asia Pacific Heart Rhythm Society
- ECG
electrocardiogram
- EHRA
European Heart Rhythm Association
- HRS
Heart Rhythm Society
- KAP
knowledge, attitude, and practice
- SOLAECE
Sociedad Latinoamericana de Estimulación CardíacayCardíaca y Electrofisiología
1. Background
Atrial fibrillation (AF) is one of the most common arrhythmias in the world, with prevalence increasing gradually at older age [1, 2]. The presence of AF significantly increases the risk of ischemic cerebral stroke and other cardiovascular complications, elevating disability and mortality rates [3, 4]. In addition, the incidence of asymptomatic AF is very high, especially in the elderly [5]. Thus, reducing the burden of AF and embolism has become one of the challenges for modern medicine. Considering that the diagnosis of AF depends on an electrocardiogram (ECG), asymptomatic AF is generally difficult to discover [6]. Screening of high‐risk populations can improve the detection rate of AF, which is helpful for early detection, diagnosis, and treatment [7].
A variety of screening options were proposed for early AF detection, but theoretical research and practical experience have indicated that individual practice depends on the knowledge and attitude of each patient [8]. Only when the acquired basic knowledge is truly absorbed can healthy practice be promoted [9]. Therefore, it is of great significance to evaluate the knowledge, attitudes, and practices (KAP) of AF and AF screening in high‐risk populations and explore their influencing factors so that targeted interventions can be implemented. A few previous KAP studies were mainly focused on the diagnosis and treatment of AF [10, 11, 12], while KAP towards screening is rarely discussed, especially among the elderly population who are harder to reach. Based on the above, this cross sectional survey was conducted to explore the KAP towards AF prevention (including oral anticoagulants, blood pressure and blood glucose monitoring, lifestyle changes) and AF screening (including different screening modalities) in high‐risk elderly participants.
2. Methods
2.1. Study Design and Participants
This cross sectional study was performed online between August 2022 and October 2022. Patients aged ≥ 65 years or with at least one of the following characteristics were included: hypertension, diabetes, coronary heart disease, chronic obstructive pulmonary disease, obstructive sleep apnea‐hypopnea syndrome, hyperthyroidism, hypothyroidism, history of AF in close family members, or heavy drinking (more than 5 drinks every day or 15 drinks per week). Exclusion criteria were the inability to fill out the online questionnaire. The study was approved by the Ethics Committee of the Aerospace Center Hospital (CR‐2021‐CHDRP (2020)‐002). Informed consent was obtained from all participants.
2.2. Procedures
A self‐administered questionnaire on AF prevention and screening was designed according to the European Heart Rhythm Association (EHRA) consensus document endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLAECE) [13], 2016 ESC Guidelines for the management of AF developed in collaboration with EACTS [14], and 2021 Asia Pacific Heart Rhythm Society (APHRS) practice guidance on AF screening [15]. The questionnaire was reviewed by 3 experts (including 2 cardiovascular experts and 1 epidemiologist) and modified according to the suggestions. The final questionnaire consisted of the following 4 dimensions: (1) demographic characteristics of participants, including 18 items; (2) knowledge dimension, including 12 questions. The correct answer to each question was given 1 point, while every wrong answer or unclear answer was given 0 points. The total score of the knowledge dimension ranged from 0 to 12 points. (3) Attitude dimension included 12 questions and was evaluated with the 5‐level Likert scale; for questions signifying positive attitudes (A1, A2, A5, A10, A12), 5 points were allocated for the “strongly agree” and 1 point for “strongly disagree”. For questions signifying negative attitudes (A3, A4, A6, A7, A8, A9, and A11), 1 point was allocated for “strongly agree” and 5 points for “strongly disagree”. The total score for 12 questions ranged from 12 to 60 points. (4) Practice dimension included 5 questions (question P5 additionally devided into 8 subquestions) and was also evaluated using the 5‐level Likert scale. The questions were scored from 5 points for “always” to 1 point for “never”, and the total score of the dimension ranged from 5 to 25 points.
The final questionnaire had a Cronbach's α of 0.913 and Kaiser‐Meyer‐Olkin (KMO) of 0.923, indicating that the high internal consistency [16]. The investigators established the online questionnaire based on the Questionnaire Star platform (https://www.wjx.cn/), after which a Quick Response (QR) code was generated. Participants were recruited from the outpatient department and wards of the hospital, as well as social media. Participants scanned the QR code to complete the questionnaire survey. The investigators assessed the completeness, internal continuity, and rationality of all questionnaires; then the trained staff reviewed the data for errors and verified data by telephone or during the next visit.
2.3. Statistical Analysis
Stata 17.0 software (Stata Corporation, College Station, TX, USA) was used for the statistical analysis. Continuous data were expressed as mean ± standard deviation (SD) and compared by t‐test. The categorical data were presented as n (%) and compared with the chi‐squared test. The KAP scores were compared between subgroups of participants with different characteristics. The Practice scores were used as the dependent variables, after which the multivariate analysis was used to explore the association of demographic characteristics with KAP. The results were categorized using 70% of the Practice scores as the cutoff [17]. Variables with p < 0.05 in the univariate model were included in the multivariate analysis. All statistical analyses were two‐sided, and p < 0.05 was considered statistically significant.
3. Results
3.1. Demographic Characteristics
A total of 863 patients with a mean age of 66.54 ± 11.00 years were enrolled, including 270 males (31.29%) and 587 females (68.02%). The Body Mass Index (BMI) of participants ranged from 15 to 37.64 kg/m2, with an average of 23.53 kg/m2. The mean scores of knowledge, attitude, and practice were 9 (range from 0 to 11), 37 (range from 23 to 57), and 21.56 (range from 5 to 25), respectively.
The higher knowledge scores were found among urban residents (p < 0.001), those with higher educational levels (p = 0.012), professional and technical staff/employees (p < 0.001), higher income groups (p < 0.001), married individuals (p = 0.019), those with social medical insurance only (p = 0.017), nonsmokers (p = 0.009), nondrinkers, and current drinkers (as compared to previous drinkers, p = 0.039), a more stable emotional state (p < 0.001), and in those who received the questionnaire at the outpatient department or ward of the hospital (p < 0.001). In terms of attitude scores, the higher attitude scores were observed in female participants (p < 0.001), those who were unmarried (p = 0.010), social medical insurance and commercial medical insurance holders (p = 0.002), nonsmokers and previous smokers (p = 0.046), current drinkers (p = 0.037), in those who reported no history of AF (p = 0.001), fair sleep (p < 0.001), and a stable emotional state (p < 0.001), as well as in those who received the questionnaire at the outpatient department or ward of the hospital (p < 0.001). As for practice scores, the higher practice scores were found in urban residents (p = 0.008), professional and technical staff (p < 0.001), higher income groups (p < 0.001), married individuals (p = 0.001), social medical insurance and commercial medical insurance holders (p = 0.034), nonsmokers (p < 0.001), nondrinkers (p < 0.001), those who reported previous AF (p < 0.001), better sleep (p < 0.001), and a highly stable emotional state (p < 0.001) and in those who received the questionnaire at the outpatient department or ward of the hospital (p < 0.001) (Table 1).
TABLE 1.
Baseline characteristics and comparison of knowledge, attitude, and practice scores between participants with different characteristics.
| Variables | n = 863 | Knowledge | Attitude | Practice | |||
|---|---|---|---|---|---|---|---|
| Score | p | Score | p | Score | p | ||
| Total score | 9 (0, 11) | 37 (23, 57) | 21.56 (5, 25) | ||||
| Age | 66.54 ± 11.00 | ||||||
| BMI | 23.53 (15, 37.64) | ||||||
| Sex | 0.067 | < 0.001 | 0.051 | ||||
| Male | 270 (31.29) | 9 (6, 9) | 34 (32, 39) | 22.05 (19, 25) | |||
| Female | 587 (68.02) | 9 (7, 9) | 38 (33, 41) | 21.43 (19.06, 24.37) | |||
| Missing | 6 (0.70) | / | / | / | |||
| Residence | < 0.001 | 0.056 | 0.008 | ||||
| Urban area | 781 (90.50) | 9 (7, 9) | 37 (32, 41) | 21.75 (19.25, 24.68) | |||
| Nonurban area | 82 (9.50) | 7 (3, 9) | 36 (32, 39) | 20.15 (16.75, 24) | |||
| Educational level | 0.012 | 0.108 | 0.073 | ||||
| Primary school or lower | 88 (10.20) | 8 (5, 9) | 36 (32, 39) | 20.75 (18.15, 24.59) | |||
| Junior middle school/senior middle school/technical secondary | 259 (30.01) | 9 (7, 9) | 37 (32, 40) | 21 (18.93, 24.31) | |||
| Junior college/college | 479 (55.50) | 9 (7, 9) | 37 (32, 41) | 21.93 (19.30, 24.68) | |||
| Postgraduate or higher | 37 (4.29) | 9 (7, 9) | 35 (32, 39) | 23 (20, 25) | |||
| Occupation | < 0.001 | 0.245 | < 0.001 | ||||
| Professional and technical staff | 415 (47.97) | 9 (8, 9) | 37 (32, 41) | 22.42 (19.93, 24.93) | |||
| Employee | 194 (22.48) | 9 (6, 9) | 36 (32, 39) | 20.77 (18.66, 24.5) | |||
| Others | 253 (29.32) | 8 (5, 9) | 37 (33, 40) | 20.73 (18.12, 24) | |||
| Missing | 2 (0.23) | ||||||
| Income (yuan) | < 0.001 | 0.518 | < 0.001 | ||||
| ≤ 5000 | 175 (20.28) | 8 (4, 9) | 37 (34, 41) | 20.46 (17.4, 23) | |||
| 5001–10,000 | 473 (54.81) | 9 (7, 9) | 37 (32, 41) | 21.93 (19.31, 24.93) | |||
| ≥ 10,001 | 210 (24.33) | 9 (7, 9) | 37 (32, 40) | 22.08 (19.87, 24.68) | |||
| Missing | 5 (0.58) | ||||||
| Marital status | 0.019 | 0.010 | 0.001 | ||||
| Unmarried | 117 (13.56) | 8 (5, 9) | 38 (35, 41) | 20.62 (18.14, 23.31) | |||
| Married | 746 (86.44) | 9 (7, 9) | 37 (32, 40) | 21.79 (19.25, 24.81) | |||
| Underlying disease* | |||||||
| Diabetes | 331 (38.35) | 9 (8, 9) | 32 (32, 37) | 24.81 (21.5, 25) | |||
| Hypertension | 459 (53.19) | 9 (7, 9) | 35 (32, 40) | 22.8 (19.66, 25) | |||
| Renal disease | 21 (2.43) | 8 (7, 9) | 32 (31, 37) | 21.62 (20.5, 24.5) | |||
| Coronary heart disease | 141 (16.34) | 9 (7, 9) | 38 (35, 41) | 20.93 (18.56, 23.06) | |||
| Bradycardiac arrhythmia | 115 (13.33) | 9 (7, 9) | 35 (32, 40) | 22.42 (19.56, 25) | |||
| Valvulopathy | 14 (1.62) | 8 (7, 9) | 37 (33, 40) | 20.87 (19.56, 24) | |||
| Hyperthyroidism | 11 (1.27) | 9 (8, 9) | 38 (32, 40) | 21.46 (19.5, 24.87) | |||
| Hypothyroidism | 41 (4.75) | 8 (8, 9) | 39 (36, 41) | 20.06 (18.06, 22.6) | |||
| Respiratory disease | 59 (6.84) | 8 (7, 9) | 37 (34, 41) | 20.46 (18, 22.56) | |||
| None | 169 (19.58) | 9 (5, 9) | 39 (35, 42) | 21 (18.81, 23.75) | |||
| Others | 122 (14.14) | 8 (5, 9) | 38 (35, 41) | 20.27 (17.93, 22.68) | |||
| Medical insurance | 0.017 | 0.002 | 0.034 | ||||
| Social medical insurance only | 796 (93.32) | 9 (7, 9) | 37 (32, 40) | 21.75 (19.06, 24.81) | |||
| Social medical insurance and commercial medical insurance | 27 (3.17) | 7 (5, 9) | 40 (36, 44) | 21.25 (18.62, 24.26) | |||
| None | 30 (3.52) | 8 (4, 9) | 39 (35, 41) | 20.07 (17.81, 23.18) | |||
| Smoking | 0.009 | 0.046 | < 0.001 | ||||
| Nonsmoker | 723 (84.66) | 9 (7, 9) | 37 (32, 40) | 22 (19.5, 24.87) | |||
| Previous smoker | 88 (10.30) | 8 (5, 9) | 37 (35, 41) | 20.03 (17.31, 22.14) | |||
| Current smoker | 43 (5.04) | 8 (5, 9) | 35 (32, 38) | 19.75 (16.43, 23.81) | |||
| Alcohol drinking | 0.039 | 0.037 | < 0.001 | ||||
| Nondrinkers | 647 (76.03) | 9 (7, 9) | 37 (32, 41) | 22.12 (19.68, 24.87) | |||
| Previous drinkers | 114 (13.40) | 8 (5, 9) | 37 (32, 39) | 20.86 (18, 23.87) | |||
| Current drinkers | 90 (10.58) | 9 (6, 9) | 38 (34, 41) | 19.84 (17, 22) | |||
| Family history of atrial fibrillation | 0.002 | 0.325 | 0.092 | ||||
| Yes | 114 (13.32) | 9 (8, 9) | 37 (32, 40) | 22.25 (19.5, 25) | |||
| No | 742 (86.68) | 9 (7, 9) | 37 (32, 41) | 21.43 (19, 24.5) | |||
| History of atrial fibrillation | < 0.001 | 0.001 | < 0.001 | ||||
| Previous atrial fibrillation | 120 (14.04) | 9 (8, 9) | 34 (32, 39) | 23 (19.5, 25) | |||
| Current atrial fibrillation | 54 (6.32) | 9 (7, 9) | 37 (32, 40) | 21.11 (18.66, 24.93) | |||
| None | 502 (58.71) | 9 (7, 9) | 38 (32, 41) | 22 (19.42, 24.62) | |||
| Unclear | 179 (20.94) | 8 (4, 9) | 37 (33, 40) | 20.31 (18, 23.5) | |||
| Sleep score | < 0.001 | < 0.001 | < 0.001 | ||||
| Very good | 232 (26.95) | 9 (9, 9) | 32 (32, 33) | 25 (24, 25) | |||
| Good | 222 (25.78) | 9 (6, 9) | 37.47 ± 4.37 | 20.87 (18.87, 23.5) | |||
| Fair | 273 (31.71) | 8 (6, 9) | 38.95 ± 4.54 | 20.18 (18.31, 22.64) | |||
| Poor/very poor | 134 (15.56) | 8 (6, 9) | 38.84 ± 4.05 | 20.62 (18, 22.5) | |||
| Emotional state | < 0.001 | < 0.001 | < 0.001 | ||||
| Highly stable | 265 (30.71) | 9 (9, 9) | 32 (32, 35) | 24.93 (23.56, 25) | |||
| Stable | 301 (34.88) | 9 (6, 9) | 38.63 ± 4.37 | 20.73 (19, 23.06) | |||
| Fair | 239 (27.69) | 8 (5, 9) | 38.46 ± 4.51 | 20.12 (17.81, 22.31) | |||
| Fluctuating | 58 (6.72) | 8 (7, 9) | 37.67 ± 3.94 | 20.53 (17.93, 23) | |||
| Questionnaire access | < 0.001 | < 0.001 | < 0.001 | ||||
| Hospital | 414 (47.97) | 9 (8, 9) | 33 (32, 39) | 24 (20.1, 25) | |||
| Social Media | 449 (52.03) | 8 (6, 9) | 38.50 ± 4.33 | 20.53 (18.14, 22.5) | |||
Note: *indicates that the question is a multiple‐choice question.
3.2. Distribution of Each Dimension
Table 2 shows the distribution and correct rate of questions in the knowledge dimension. It was found that some items had a higher correct rate, while other items had a very low correct rate, which should be greatly improved. Notably, the correct rate of “The incidence of atrial fibrillation increases with age.” (K3), “Atrial fibrillation can elevate the risk of ischemic stroke and systemic circulation arterial embolism.” (K5), and “The screening of asymptomatic atrial fibrillation can only be done in hospital.” (K12) was < 10%.
TABLE 2.
Knowledge dimension.
| Statements | n (%) | |
|---|---|---|
| Correct | Wrong/Unclear | |
| K1. Atrial fibrillation is one of the most common arrhythmias in clinical practices. | 664 (76.94) | 199 (23.06) |
| K2. Atrial fibrillation includes paroxysmal, persistent, and permanent atrial fibrillation. | 603 (70.20) | 256 (29.80) |
| K3. The incidence of atrial fibrillation increases with age. | 51 (5.93) | 809 (94.07) |
| K4. The major manifestation of atrial fibrillation includes palpitation, discomfort in the chest, shortness of breath, and fatigue. | 686 (79.77) | 174 (20.23) |
| K5. Atrial fibrillation can elevate the risk of ischemic stroke and systemic circulation arterial embolism. | 11 (1.28) | 850 (98.72) |
| K6. The three key measurements of preventing atrial fibrillation are to prevent stroke, control the ventricular rate, and control the cardiac rhythm. | 659 (76.36) | 204 (23.64) |
| K7. Modifying unhealthy lifestyles could reduce the risk of atrial fibrillation. | 729 (84.97) | 129 (15.03) |
| K8. Atrial fibrillation screening could help in the early detection, diagnosis, and treatment of atrial fibrillation. | 788 (91.73) | 71 (8.27) |
| K9. Managing hypertension, smoking, and alcohol drinking can reduce the risk of atrial fibrillation. | 768 (89.30) | 92 (10.70) |
| K10. Detecting and diagnosing asymptomatic atrial as early as possible is critical for oral anticoagulation therapy and stroke prevention. | 712 (82.79) | 148 (17.21) |
| K11. Screening is especially important for patients with asymptomatic atrial fibrillation. | 768 (89.41) | 91 (10.59) |
| K12. The screening of asymptomatic atrial fibrillation can only be done in the hospital. | 29 (3.38) | 830 (96.62) |
The participants' attitudes towards AF prevention and screening are presented in Figure 1. Nearly 90% of the participants considered AF as a dangerous disease that reduces patients' quality of life (agree or completely agree); 60%–70% of the participants considered anticoagulation treatment for AF to be complex and prolonged therapy. Furthermore, the majority of the participants considered AF screening beneficial and would like to learn more about AF and AF screening. However, about half of the participants worried that AF screening was too expensive and insufficient to detect any problems. Additionally, 46% of the participants considered regular screening to be time‐consuming, and 85% hoped to have the possibility to undergo AF screening at any time without going to the hospital.
FIGURE 1.
The score distribution of “attitude” dimension. (A1) I believe atrial fibrillation is a dangerous disease (P); (A2) I believe atrial fibrillation can reduce patients' quality of life (P); (A3) I think the precautions for taking anticoagulants are too complicated (N); (A4) I think anticoagulation therapy for atrial fibrillation takes too long (N); (A5) I can benefit from atrial fibrillation screening (P); (A6) I'm worried that atrial fibrillation screening will be too expensive (N); (A7) I am worried that atrial fibrillation screening will not be useful (N); (A8) I am terrified of having atrial fibrillation (N); (A9) I think a regular review of atrial fibrillation is a waste of time (N); (A10) I would be more than willing to do so if I could be screened at any time without going to the hospital (P); (A11) I would be very anxious if the screening result was positive (N); (A12) I would like to learn more about atrial fibrillation and atrial fibrillation screening (P).The score distribution of “attitude” dimension. (A1) I believe atrial fibrillation is a dangerous disease (P); (A2) I believe atrial fibrillation can reduce patients' quality of life (P); (A3) I think the precautions for taking anticoagulants are too complicated (N); (A4) I think anticoagulation therapy for atrial fibrillation takes too long (N); (A5) I can benefit from atrial fibrillation screening (P); (A6) I'm worried that atrial fibrillation screening will be too expensive (N); (A7) I am worried that atrial fibrillation screening will not be useful (N); (A8) I am terrified of having atrial fibrillation (N); (A9) I think a regular review of atrial fibrillation is a waste of time (N); (A10) I would be more than willing to do so if I could be screened at any time without going to the hospital (P); (A11) I would be very anxious if the screening result was positive (N); (A12) I would like to learn more about atrial fibrillation and atrial fibrillation screening (P).
Figure 2 shows the practice of the participants towards AF and AF screening. About 74% of the participants were willing to take oral anticoagulants if recommended by their doctors. To prevent the occurrence of AF, approximately 80% of the participants agreed to strictly monitor blood pressure and blood sugar, quit smoking and alcohol consumption, and exercise at an appropriate intensity. The participants showed that they were willing to accept the following AF screening methods if necessary: the top three were blood pressure (heart rate) testing, pulse palpation, and ECG (resting 12‐lead ECG); the bottom three were smartphone combined with the application (app), smart watch/bracelet combined with the app, and wearable vest.
FIGURE 2.
The score distribution of “practice” dimension. (P1) I will take oral anticoagulants if recommended by my doctor; (P2) I will closely monitor the blood pressure and blood glucose level to prevent the occurrence of atrial fibrillation; (P3) I can quit smoking and drinking alcohol to prevent the occurrence of atrial fibrillation; (P4) I will exercise with appropriate intensity to prevent the occurrence of atrial fibrillation; (P5) Are you willing to use the following AF screening modalities: P5.1 pulse palpation; P5.2 blood pressure (heart rate) measurement; P5.3 electrocardiogram (resting 12‐lead electrocardiogram); P5.4 dynamic electrocardiogram (a small box over the shoulder); P5.5 echocardiography; P5.6 smartphone + APP; P5.7 smartwatch/bracelet +APP; P5.8 wearable vest. The score distribution of “practice” dimension. (P1) I will take oral anticoagulants if recommended by my doctor; (P2) I will closely monitor the blood pressure and blood glucose level to prevent the occurrence of atrial fibrillation; (P3) I can quit smoking and drinking alcohol to prevent the occurrence of atrial fibrillation; (P4) I will exercise with appropriate intensity to prevent the occurrence of atrial fibrillation; (P5) Are you willing to use the following AF screening modalities: P5.1 pulse palpation; P5.2 blood pressure (heart rate) measurement; P5.3 electrocardiogram (resting 12‐lead electrocardiogram); P5.4 dynamic electrocardiogram (a small box over the shoulder); P5.5 echocardiography; P5.6 smartphone + APP; P5.7 smartwatch/bracelet +APP; P5.8 wearable vest.
There were no significant differences in knowledge, attitude, and practice between participants aged 65 years or younger and those older than 65 years (Figure 3).
FIGURE 3.
Distribution of KAP scores for participants ≤ 65 versus > 65 years. Distribution of KAP scores for participants ≤ 65 versus > 65 years.
3.3. Multivariate Logistic Regression of Practice
The multivariate logistic regression analysis showed that only knowledge (OR = 1.159, 95% confidence interval (CI): 1.045–1.286, p = 0.005) was independently associated with better practice, while attitude (OR = 0.861, 95% CI: 0.817–0.908, p < 0.001), reported poor sleep quality (OR = 0.529, 95% CI: 0.259–1.0788, p = 0.010), fair (OR = 0.234, 95% CI: 0.111–0.491, p < 0.001) or fluctuating (OR = 0.321, 95% CI: 0.103–0.996, p = 0.049) emotional state, and acquiring the questionnaire through social media (OR = 0.419, 95% CI: 0.268–0.653, p < 0.001) were independently associated with worse practice (Table 3).
TABLE 3.
Univariate and multivariate logistic regression analysis of pratice*.
| Variables | Univariate logistic regression | Multivariate logistic regression | ||
|---|---|---|---|---|
| OR (95% CI) | p | OR (95% CI) | p | |
| Knowledge score | 1.401 (1.270, 1.544) | < 0.001 | 1.159 (1.045, 1.286) | 0.005 |
| Attitude score | 0.746 (0.711, 0.782) | < 0.001 | 0.861 (0.817, 0.908) | < 0.001 |
| Age | 0.999 (0.986, 1.012) | 0.954 | ||
| BMI | 1.082 (1.032, 1.135) | 0.001 | 1.056 (0.983, 1.135) | 0.131 |
| Sex | ||||
| Male | Ref. | Ref. | ||
| Female | 0.616 (0.453, 0.837) | 0.002 | 0.687 (0.397, 1.189) | 0.180 |
| Residence | ||||
| Urban area | Ref. | |||
| Nonurban area | 0.731 (0.432, 1.238) | 0.245 | ||
| Educational level | ||||
| Primary school or lower | Ref. | |||
| Junior middle school/senior middle school/technical secondary school | 0.963 (0.559, 1.659) | 0.893 | ||
| Junior college/college | 1.219 (0.734, 2.025) | 0.443 | ||
| Postgraduate or higher | 2.031 (0.911, 4.530) | 0.083 | ||
| Occupation | ||||
| Others | Ref. | Ref. | ||
| Professional and technical staff | 2.024 (1.412, 2.900) | < 0.001 | 1.191 (0.715, 1.983) | 0.501 |
| Employee | 1.424 (0.924, 2.194) | 0.109 | 0.807 (0.441, 1.475) | 0.487 |
| Income | ||||
| ≤ 5000 | Ref. | Ref. | ||
| (5001–10,000) | 2.622 (1.686, 4.077) | < 0.001 | 1.049 (0.590, 1.867) | 0.869 |
| ≥ 10,001 | 2.410 (1.473, 3.944) | < 0.001 | 1.144 (0.595, 2.199) | 0.686 |
| Marital status | ||||
| Married | Ref. | Ref. | ||
| Unmarried | 0.466 (0.284, 0.767) | 0.003 | 0.833 (0.427, 1.625) | 0.593 |
| Smoking | ||||
| Nonsmoker | Ref. | Ref. | ||
| Previous smoker | 0.259 (0.132, 0.510) | < 0.001 | 0.437 (0.175, 1.088) | 0.076 |
| Current smoker | 0.613 (0.297, 1.266) | 0.186 | 0.604 (0.203, 1.795) | 0.365 |
| Alcohol drinking | ||||
| Nondrinker | Ref. | Ref. | ||
| Previous drinker | 0.451 (0.275, 0.738) | 0.002 | 0.901 (0.436, 1.864) | 0.781 |
| Current drinker | 0.290 (0.154, 0.544) | < 0.001 | 0.443 (0.186, 1.052) | 0.065 |
| Family history of atrial fibrillation | ||||
| No | Ref. | Ref. | ||
| Yes | 1.619 (1.077, 2.435) | 0.02 | 1.164 (0.630, 2.152) | 0.627 |
| History of atrial fibrillation | ||||
| No atrial fibrillation | Ref. | |||
| Previous atrial fibrillation | 1.492 (0.989, 2.252) | 0.056 | ||
| Current atrial fibrillation | 0.861 (0.460, 1.608) | 0.639 | ||
| Unclear | 0.603 (0.402, 0.904) | 0.015 | ||
| Sleep score | ||||
| Very good | Ref. | Ref. | ||
| Good | 0.076 (0.048, 0.121) | < 0.001 | 0.313 (0.167, 0.588) | < 0.001 |
| Fair | 0.062 (0.039, 0.097) | < 0.001 | 0.529 (0.259, 1.078) | 0.080 |
| Poor/very poor | 0.036 (0.018, 0.070) | < 0.001 | 0.292 (0.115, 0.743) | 0.010 |
| Emotional state | ||||
| Highly stable | Ref. | Ref. | ||
| Stable | 0.089 (0.059, 0.133) | < 0.001 | 0.370 (0.203, 0.673) | 0.001 |
| Fair | 0.052 (0.032, 0.086) | < 0.001 | 0.234 (0.111, 0.491) | < 0.001 |
| Fluctuating | 0.064 (0.028, 0.148) | < 0.001 | 0.321 (0.103, 0.996) | 0.049 |
| Obtaining access | ||||
| Hospital | Ref. | Ref. | ||
| Social Media | 0.152 (0.108, 0.214) | < 0.001 | 0.419 (0.268, 0.653) | < 0.001 |
The 70% of the Practice score, i.e., 24.25, was used as the cutoff for the classification of the Practice score.
4. Discussion
Study participants from high‐risk AF population demonstrated acceptable practice towards AF screening and prevention. Findings suggest that better knowledge might result in better practice, while better attitudes do not always influence practice. Additionally, poor sleep and fluctuating emotional states may result in lower practice scores. These findings may help to develop targeted education measures to improve the screening adherence and AF prevention strategies in the high‐risk elderly population.
In this study, 70% of questions regarding the AF prevention and AF screening knowledge were correctly answered, and the total knowledge scores were relatively promising. However, there was insufficient understanding of some issues, such as the relationship between AF and age, the relationship between AF and ischemic stroke or arterial embolism. This was consistent with previous studies, which also demonstrated significant knowledge gaps among patients, caregivers, and physicians in relation to AF and stroke management [18, 19, 20, 21, 22], suggesting that additional health education is needed in this population. It was found that knowledge scores significantly differed according to residence area, educational level, occupation, income, marital status, medical insurance, smoking, alcohol drinking, emotional state, and means of acquiring the questionnaire, outlining the characteristics of the most vulnerable subpopulations in need of educational intervention. In particular, medical resources are concentrated in urban areas; thus, rural residents have fewer possibilities to increase their health‐related knowledge. Those with higher education levels, higher income, higher occupational technology, and better medical security pay more attention to medical information and health knowledge and might better understand AF and AF screening, while others need special guidance. Consistent with the results obtained in the studies on self‐management of AF patients [23, 24, 25], individuals with a lack of educational opportunities and lower income are more vulnerable to the lack of knowledge, which could lead to the lack of understanding towards AF screening as well. Conversely, according to another study, higher income level is not always associated with increased AF knowledge [21], and other factors might play a more notable role.
From the score distribution of the attitude dimension, it was observed that most participants had some awareness about the risk of AF, the importance and advantage of AF screening, and the need for learning more about AF and AF screening. However, some participants believed that the anticoagulation treatment of AF is complex and a long therapy, which may influence their willingness to take AF prevention measures. In another study, Obamiro et al. [26] also reported knowledge/attitude gaps in patients with AF taking oral anticoagulants, making it an important topic for health education. Additionally, some participants were concerned about the cost of AF screening, while others believed that the procedure is time‐consuming and useless, confirming that doubts expressed in other populations [27, 28, 29] are relevant for these study participants as well. Therefore, education regarding screening modalities that reduce cost, save time, and have a reliable detection rate can contribute to improving AF screening practice.
Practice aspects discussed in the present study included willingness to receive oral anticoagulants if prescribed by a doctor, monitoring the blood pressure and blood glucose level, and changing lifestyle habits (moderate physical exercises, quitting smoking and alcohol) to prevent the AF occurrence; in addition, a number of methods were proposed to assess the preference for screening modalities. Similar to the knowledge scores, the lower practice scores were found in the participants who were nonurban residents, lower income group, unmarried, without insurance, current smokers, current drinkers, poor/very poor sleep, and fluctuating emotional state. Many of those categories were also previously reported to have greater AF hospitalization risk [30], stressing the importance of targeted health education. Besides, the results of logistic regression analysis confirmed the association between the participants' knowledge and practice scores, suggesting that targeted interventions to strengthen knowledge could be effective in changing inappropriate behaviors. As recommended by other studies, some interventions can be accomplished at the individual level, such as quitting smoking and drinking, improving sleep quality, and maintaining a relatively stable emotional state [31, 32, 33]. Other interventions should be implemented by the government or medical institutions, including raising the income of residents, improving health insurance, and conducting health education through public welfare activities. Unlike the relationship between knowledge and practice, attitude and practice scores of AF and AF screening were negatively correlated in this study. This may be at least partly attributed to the complex relationship between screening costs and other worries that were expressed in the attitude section, with participants preferring to understand AF screening methods but needing to comply with the hospital requirements [29]. Although Steinhubl et al. [34] reported that immediate monitoring with a home‐based wearable device had a higher rate of AF diagnosis in comparison to delayed monitoring, wearable devices were the least acceptable screening modality in the present study. It is interesting to note that this study accessed a population of older, predominantly female individuals, and the screening methods least acceptable to the population involved newer technology (smartphone applications, wearing smart watch/bracelet, etc.); it is in agreement with the previous study reporting women aged ≥ 50 years less likely to use technology apps for healthcare advice/screening [35]. However, another recent study points out that attitudes of the elderly population towards new screening technologies such as smart watches are not necessarily negative– it is usability and accessibility that concern potential older users [36]. Based on that, future educational interventions should consider barriers for the use of newer technology in the discussed population.
The present study has a few limitations. First, although the findings were valuable for providing targeted improvement measures and interventions for AF prevention and screening, we could not assess and describe the effects of interventions on improving the KAP among the high‐risk population due to the characteristics of the cross sectional design. Moreover, the study's results were mainly based on self‐reported instruments, which may introduce reporting bias and overestimate real results. Although the questionnaire demonstrated high internal consistency, participants who were recruited from social media had lower knowledge/practice but significantly higher attitude scores, which might have affected the total results, and a higher rate of deviations from truth without the direct guidance of researchers is not excluded. Finally, the participants in this study were older than 60 years Thus, the results were not applicable to those at high risk of AF at other ages.
5. Conclusions
In summary, although the high‐risk elderly population has acquired partial knowledge of AF prevention and screening, their practice could be improved. Several factors can influence the AF prevention and screening practice, including knowledge and attitude, sleep quality, and emotional state. In addition, an appropriate screening modality is critical to improve AF screening practice. Therefore, targeted strengthening of knowledge education related to AF prevention and screening, establishing a correct attitude towards the topic, and selecting appropriate screening methods could help promote better practices.
Author Contributions
Z.Y., S.X., and X.Y. carried out the studies, participated in collecting data, and drafted the manuscript. L.Z., L.J., S.R., X.Z., and R.L. performed the statistical analysis and participated in its design. J.Q., C.W., N.G., Y.W., and D.G. participated in the acquisition, analysis, or interpretation of data. All authors read and approved the final manuscript.
Ethics Statement
This work has been carried out in accordance with the Declaration of Helsinki (2000) of the World Medical Association. The study was approved by the Ethics Committee of the Aerospace Center Hospital (CR‐2021‐CHDRP (2020)‐002). Informed consent was obtained from all the participants.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors have nothing to report.
Funding: This work was supported by the Capital Development Research Fund for the study Effectiveness of Early Systemic Screening in Older Atrial Fibrillation Patients in Communities (First publication: 2020‐3‐6081).
Data Availability Statement
All data generated or analyzed during this study are included in this published article and its Supporting Information files.
References
- 1. Eagle K. A., Cannom D. S., and Garcia D. A., “Management of Atrial Fibrillation: Translating Clinical Trial Data Into Clinical Practice,” American Journal of Medicine 124 (2011): 4–14, 10.1016/j.amjmed.2010.05.016. [DOI] [PubMed] [Google Scholar]
- 2. Chen Q., Yi Z., and Cheng J., “Atrial Fibrillation in Aging Population,” Aging Medicine (Milton) 1 (2018): 67–74, 10.1002/agm2.12015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Katsanos A. H., Kamel H., Healey J. S., and Hart R. G., “Stroke Prevention in Atrial Fibrillation: Looking Forward,” Circulation 142 (2020): 2371–2388, 10.1161/circulationaha.120.049768. [DOI] [PubMed] [Google Scholar]
- 4. Kamel H. and Healey J. S., “Cardioembolic Stroke,” Circulation Research 120 (2017): 514–526, 10.1161/circresaha.116.308407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Langer A., Healey J. S., Quinn F. R., et al., “Detection of Atrial Fibrillation in Asymptomatic At‐Risk Individuals,” International Journal of Cardiology 334 (2021): 55–57, 10.1016/j.ijcard.2021.04.048. [DOI] [PubMed] [Google Scholar]
- 6. Ballatore A., Matta M., Saglietto A., et al., “Subclinical and Asymptomatic Atrial Fibrillation: Current Evidence and Unsolved Questions in Clinical Practice,” Medicina (Kaunas, Lithuania) 55 (2019): 497, 10.3390/medicina55080497. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Svennberg E., Friberg L., Frykman V., Al‐Khalili F., Engdahl J., and Rosenqvist M., “Clinical Outcomes in Systematic Screening for Atrial Fibrillation (STROKESTOP): A Multicentre, Parallel Group, Unmasked, Randomised Controlled Trial,” Lancet 398 (2021): 1498–1506, 10.1016/s0140-6736(21)01637-8. [DOI] [PubMed] [Google Scholar]
- 8. Dauda Goni M., Hasan H., Naing N. N., et al., “Assessment of Knowledge, Attitude and Practice Towards Prevention of Respiratory Tract Infections Among Hajj and Umrah Pilgrims From Malaysia in 2018,” International Journal of Environmental Research and Public Health 16 (2019): 4569, 10.3390/ijerph16224569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Nasir B. B., Buseir M. S., and Muhammed O. S., “Knowledge, Attitude and Practice Towards Insulin Self‐Administration and Associated Factors Among Diabetic Patients at Zewditu Memorial Hospital, Ethiopia,” PLoS One 16 (2021): e0246741, 10.1371/journal.pone.0246741. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Xu W., Sun G., Lin Z., et al., “Knowledge, Attitude, and Behavior in Patients With Atrial Fibrillation Undergoing Radiofrequency Catheter Ablation,” Journal of Interventional Cardiac Electrophysiology 28 (2010): 199–207, 10.1007/s10840-010-9496-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Ye S., Wang T., Liu A., Yu Y., Pan Z., and Gu J., “A Study of Knowledge, Attitudes, and Practices of Primary Care Physicians Toward Anticoagulant Therapy in Patients With Non‐Valvular Atrial Fibrillation in Shanghai, China,” BMC Family Practice 21, no. 1 (2020): 165, 10.1186/s12875-020-01236-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Amara W., Larsen T. B., Sciaraffia E., et al., “Patients' Attitude and Knowledge About Oral Anticoagulation Therapy: Results of a Self‐Assessment Survey in Patients With Atrial Fibrillation Conducted by the European Heart Rhythm Association,” Europace 18 (2016): 151–155, 10.1093/europace/euv317. [DOI] [PubMed] [Google Scholar]
- 13. Katritsis D. G., Boriani G., Cosio F. G., et al., “European Heart Rhythm Association (EHRA) Consensus Document on the Management of Supraventricular Arrhythmias, Endorsed by Heart Rhythm Society (HRS), Asia‐Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardiaca y Electrofisiologia (SOLAECE),” Europace 19 (2017): 465–511, 10.1093/europace/euw301. [DOI] [PubMed] [Google Scholar]
- 14. Hindricks G., Potpara T., Dagres N., et al., “2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation Developed in Collaboration With the European Association for Cardio‐Thoracic Surgery (EACTS): The Task Force for the Diagnosis and Management of Atrial Fibrillation of the European Society of Cardiology (ESC) Developed With the Special Contribution of the European Heart Rhythm Association (EHRA) of the ESC,” European Heart Journal 42 (2021): 373–498, 10.1093/eurheartj/ehaa612. [DOI] [PubMed] [Google Scholar]
- 15. Chan N. Y., Orchard J., Agbayani M. J., et al., “2021 Asia Pacific Heart Rhythm Society (APHRS) Practice Guidance on Atrial Fibrillation Screening,” Journal of Arrhythmia 38 (2022): 31–49, 10.1002/joa3.12669. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Liu Q., Huang C., Jiang L., Qiu H., and Xu J., “Reliability and Validity of Healthy Fitness Measurement Scale (V1.0) for Evaluating Healthy Fitness of College Students in Guangzhou,” Nan Fang Yi Ke Da Xue Xue Bao 41 (2021): 47–54, 10.12122/j.issn.1673-4254.2021.01.06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lee F. and Suryohusodo A. A., “Knowledge, Attitude, and Practice Assessment Toward COVID‐19 Among Communities in East Nusa Tenggara, Indonesia: A Cross‐Sectional Study,” Frontiers in Public Health 10 (2022): 957630, 10.3389/fpubh.2022.957630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Frankel D. S., Parker S. E., Rosenfeld L. E., and Gorelick P. B., “HRS/NSA 2014 Survey of Atrial Fibrillation and Stroke: Gaps in Knowledge and Perspective, Opportunities for Improvement,” Journal of Stroke and Cerebrovascular Diseases 24 (2015): 1691–1700, 10.1016/j.jstrokecerebrovasdis.2015.06.026. [DOI] [PubMed] [Google Scholar]
- 19. Desteghe L., Engelhard L., Raymaekers Z., et al., “Knowledge Gaps in Patients With Atrial Fibrillation Revealed by a New Validated Knowledge Questionnaire,” International Journal of Cardiology 223 (2016): 906–914, 10.1016/j.ijcard.2016.08.303. [DOI] [PubMed] [Google Scholar]
- 20. Reading S. R., Go A. S., Fang M. C., et al., “Health Literacy and Awareness of Atrial Fibrillation,” Journal of the American Heart Association 6 (2017): 6, 10.1161/jaha.116.005128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. LaRosa A. R., Pusateri A. M., Althouse A. D., Mathier A. S., Essien U. R., and Magnani J. W., “Mind the Gap: Deficits in Fundamental Disease‐Specific Knowledge in Atrial Fibrillation,” International Journal of Cardiology 292 (2019): 272–276, 10.1016/j.ijcard.2019.06.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Aliot E., Breithardt G., Brugada J., et al., “An International Survey of Physician and Patient Understanding, Perception, and Attitudes to Atrial Fibrillation and Its Contribution to Cardiovascular Disease Morbidity and Mortality,” Europace 12 (2010): 626–633, 10.1093/europace/euq109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. McCabe P. J., Schad S., Hampton A., and Holland D. E., “Knowledge and Self‐Management Behaviors of Patients With Recently Detected Atrial Fibrillation,” Heart & Lung 37 (2008): 79–90, 10.1016/j.hrtlng.2007.02.006. [DOI] [PubMed] [Google Scholar]
- 24. Frewen J., Finucane C., Cronin H., et al., “Factors That Influence Awareness and Treatment of Atrial Fibrillation in Older Adults,” QJM 106 (2013): 415–424, 10.1093/qjmed/hct060. [DOI] [PubMed] [Google Scholar]
- 25. McCabe P. J., Stuart‐Mullen L. G., McLeod C. J., et al., “Patient Activation for Self‐Management Is Associated With Health Status in Patients With Atrial Fibrillation,” Patient Preference and Adherence 12 (2018): 1907–1916, 10.2147/ppa.S172970. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Obamiro K. O., Chalmers L., Lee K., Bereznicki B. J., and Bereznicki L. R. E., “Anticoagulation Knowledge in Patients With Atrial Fibrillation: An Australian Survey,” International Journal of Clinical Practice 72 (2018): e13072, 10.1111/ijcp.13072. [DOI] [PubMed] [Google Scholar]
- 27. Curry S. J., Krist A. H., Owens D. K., et al., “Screening for Atrial Fibrillation With Electrocardiography: US Preventive Services Task Force Recommendation Statement,” JAMA 320 (2018): 478–484, 10.1001/jama.2018.10321. [DOI] [PubMed] [Google Scholar]
- 28. Moran P. S., Flattery M. J., Teljeur C., Ryan M., and Smith S. M., Effectiveness of Systematic Screening for the Detection of Atrial Fibrillation (Cochrane Database of Systematic Reviews, 2013), CD009586, 10.1002/14651858.CD009586.pub2. [DOI] [PubMed] [Google Scholar]
- 29. Welton N. J., McAleenan A., Thom H. H., et al., “Screening Strategies for Atrial Fibrillation: A Systematic Review and Cost‐Effectiveness Analysis,” Health Technology Assessment 21 (2017): 1–236, 10.3310/hta21290. [DOI] [PubMed] [Google Scholar]
- 30. Tertulien T., Chen Y., Althouse A. D., Essien U. R., Johnson A., and Magnani J. W., “Association of Income and Educational Attainment in Hospitalization Events in Atrial Fibrillation,” American Journal of Preventive Cardiology 7 (2021): 100201, 10.1016/j.ajpc.2021.100201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. O'Keefe E. L., Sturgess J. E., O'Keefe J. H., Gupta S., and Lavie C. J., “Prevention and Treatment of Atrial Fibrillation via Risk Factor Modification,” American Journal of Cardiology 160 (2021): 46–52, 10.1016/j.amjcard.2021.08.042. [DOI] [PubMed] [Google Scholar]
- 32. Menezes A. R., Lavie C. J., DiNicolantonio J. J., et al., “Atrial Fibrillation in the 21st Century: A Current Understanding of Risk Factors and Primary Prevention Strategies,” Mayo Clinic Proceedings 88 (2013): 394–409, 10.1016/j.mayocp.2013.01.022. [DOI] [PubMed] [Google Scholar]
- 33. Voskoboinik A., Kalman J. M., De Silva A., et al., “Alcohol Abstinence in Drinkers With Atrial Fibrillation,” New England Journal of Medicine 382 (2020): 20–28, 10.1056/NEJMoa1817591. [DOI] [PubMed] [Google Scholar]
- 34. Steinhubl S. R., Waalen J., Edwards A. M., et al., “Effect of a Home‐Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial,” JAMA 320 (2018): 146–155, 10.1001/jama.2018.8102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Lennox‐Chhugani N., Chen Y., Pearson V., Trzcinski B., and James J., “Women's Attitudes to the Use of AI Image Readers: A Case Study From a National Breast Screening Programme,” BMJ Health & Care Informatics 28 (2021): e100293, 10.1136/bmjhci-2020-100,293. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Manini T. M., Mendoza T., Battula M., et al., “Perception of Older Adults Toward Smartwatch Technology for Assessing Pain and Related Patient‐Reported Outcomes: Pilot Study,” JMIR mHealth and uHealth 7 (2019): e10044, 10.2196/10044. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analyzed during this study are included in this published article and its Supporting Information files.